Malaysia Covid19 Case Updates

Prepared By: Zahiruddin Zahidanishah

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import dataframe_image as dfi
import adjustText as aT
from jupyterthemes import jtplot
#import requests
import cufflinks as cf
#from bs4 import BeautifulSoup as bs
jtplot.style(theme='monokai', context='notebook', ticks=True, grid=False) 
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot 
init_notebook_mode(connected=True)
cf.go_offline()

from IPython.display import display_html
def display_side_by_side(*args):
    html_str=''
    for df in args:
        html_str+=df.to_html()
    display_html(html_str.replace('table','table style="display:inline"'),raw=True)

#Covid19 Malaysia States Dataset from MOH, Malaysia Github accounts 
df_states_cases = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_state.csv')
df_states_deaths = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/deaths_state.csv')
df_states_hosp = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/hospital.csv')
df_states_icu = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/icu.csv')
df_states_pkrc = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/pkrc.csv')
df_states_test = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/tests_state.csv')
df_states_vaksin = pd.read_csv('https://raw.githubusercontent.com/CITF-Malaysia/citf-public/main/vaccination/vax_state.csv')
df_states_pop = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/static/population.csv')

df_hosp_daily = df_states_hosp.groupby('date').sum().reset_index()
df_hosp_daily['admitted_covid_cum'] = df_hosp_daily['admitted_covid'].cumsum()
df_hosp_daily['discharged_covid_cum'] = df_hosp_daily['discharged_covid'].cumsum()
df_icu_daily = df_states_icu.groupby('date').sum().reset_index()
df_icu_daily['icu_covid_cum'] = df_icu_daily['icu_covid'].cumsum()
df_pkrc_daily = df_states_pkrc.groupby('date').sum().reset_index()
df_pkrc_daily['admitted_covid_cum'] = df_pkrc_daily['admitted_covid'].cumsum()
df_pkrc_daily['discharged_covid_cum'] = df_pkrc_daily['discharged_covid'].cumsum()

#Selecting the required columns
df_states_cases = df_states_cases[['date','state','cases_new','cases_recovered','cases_pvax','cases_fvax']]
df_date_start = df_states_cases.head(1)
df_date_end = df_states_cases.tail(1)
df_states_deaths = df_states_deaths[['date','state','deaths_new_dod','deaths_bid_dod','deaths_pvax','deaths_fvax']]
df_states_hosp = df_states_hosp[['date','state','admitted_covid','discharged_covid']]
df_states_vaksin = df_states_vaksin[['date','state','daily_partial','daily_full','daily_booster','daily']]
df_states_pop = df_states_pop[['state','pop']]

#Grouping and summation the detasets based on states columns
df_cases = df_states_cases.groupby('state').sum()
df_deaths = df_states_deaths.groupby('state').sum()
df_hosp = df_states_hosp.groupby('state').sum()
df_vaksin = df_states_vaksin.groupby('state').sum()

#Dataset for overall Malaysia Covid19
df_mas_cases = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_malaysia.csv')
df_mas_deaths = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/deaths_malaysia.csv')
df_mas_test = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/tests_malaysia.csv')
df_mas_vaksin = pd.read_csv('https://raw.githubusercontent.com/CITF-Malaysia/citf-public/main/vaccination/vax_malaysia.csv')
df_mas_pop = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/static/population.csv')
df_mas_aefi = pd.read_csv('https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/vaccination/aefi.csv')

df_mas_vaksin['Total Pfizer'] = df_mas_vaksin['pfizer1'] + df_mas_vaksin['pfizer2']
df_mas_vaksin['Total Sinovac'] = df_mas_vaksin['sinovac1'] + df_mas_vaksin['sinovac2']
df_mas_vaksin['Total AstraZ'] = df_mas_vaksin['astra1'] + df_mas_vaksin['astra2']
df_mas_vaksin['Cum. Daily'] = df_mas_vaksin['daily_full'].cumsum()
df_mas_vaksin['Population'] = df_states_pop.at[df_states_pop.index[0],'pop']
df_mas_vaksin['Vax. (%)'] = (df_mas_vaksin['Cum. Daily']/df_mas_vaksin['Population'])*100

df_mas_cases = df_mas_cases[['date','cases_new','cases_recovered','cases_pvax','cases_fvax','cases_active']]
df_mas_deaths = df_mas_deaths[['date','deaths_new_dod','deaths_bid_dod','deaths_pvax','deaths_fvax']]
df_mas_vaksin = df_mas_vaksin[['date', 'daily_partial', 'daily_full', 'daily_booster', 'daily', 'Total Pfizer',
                               'Total Sinovac', 'Total AstraZ', 'cansino', 'Cum. Daily', 'Vax. (%)']]

df_mas = pd.merge(df_mas_cases, df_mas_deaths, on='date')

df_mas = pd.merge(df_mas, df_mas_vaksin, on='date')

#Merging the datasets into one datasets
df_states = pd.merge(df_cases,df_deaths,on='state')

df_states = pd.merge(df_states,df_hosp,on='state')

df_states = pd.merge(df_states,df_vaksin,on='state')

df_states = pd.merge(df_states,df_states_pop,on='state')

#Creating main tables for presentation
df_states_table = df_states
df_states_table['deaths_vax_total'] = df_states_table['deaths_pvax'] + df_states_table['deaths_fvax']
df_states_table['cases_vax_total'] = df_states_table['cases_pvax'] + df_states_table['cases_fvax']

df_states_table = df_states_table[['state','cases_new','cases_recovered','cases_vax_total','deaths_new_dod',
                                  'deaths_bid_dod','deaths_vax_total','daily_partial','daily_full','daily',
                                   'daily_booster','pop']]

df_states_table.loc['Total'] = df_states_table.sum(numeric_only=True, axis=0)
df_states_table['state'] = df_states_table['state'].replace(np.nan, 'Malaysia')

df_states_table['Cases (%)'] = df_states_table['cases_new']/df_states_table['pop']*100
df_states_table['Cases Recovered (%)'] = df_states_table['cases_recovered']/df_states_table['cases_new']*100
df_states_table['Cases Vax. (%)'] = df_states_table['cases_vax_total']/df_states_table['cases_new']*100
df_states_table['Deaths (%)'] = df_states_table['deaths_new_dod']/df_states_table['cases_new']*100
df_states_table['Deaths Vax. (%)'] = df_states_table['deaths_vax_total']/df_states_table['deaths_new_dod']*100
df_states_table['Deaths BID (%)'] = df_states_table['deaths_bid_dod']/df_states_table['deaths_new_dod']*100
df_states_table['Vax. 3D (%)'] = df_states_table['daily_booster']/df_states_table['pop']*100
df_states_table['Vax. 2D (%)'] = df_states_table['daily_full']/df_states_table['pop']*100
df_states_table['Vax. 1D (%)'] = df_states_table['daily_partial']/df_states_table['pop']*100

df_states_table.rename(columns={'state':'States','cases_new':'Cases Positive','cases_recovered':'Cases Recovered',
                               'cases_vax_total':'Cases Vax.','deaths_new_dod':'Death Cases',
                               'deaths_bid_dod':'Death BID','deaths_vax_total':'Deaths Vax.',
                               },inplace=True)#'daily':'Total Vaccinated','pop':'Population'

print('Report Start Date :', df_date_start.at[df_date_start.index[0],'date'])
print('Report End Date   :', df_date_end.at[df_date_end.index[0],'date'])

Report Start Date : 2020-01-25
Report End Date   : 2022-03-19

df_mas_cases['total_vax'] = df_mas_cases['cases_pvax'] + df_mas_cases['cases_fvax']
df_mas_cases['cum_new'] = df_mas_cases['cases_new'].cumsum()
df_mas_cases['cum_recovered'] = df_mas_cases['cases_recovered'].cumsum()
df_mas_cases['cum_vax'] = df_mas_cases['total_vax'].cumsum()
df_mas_cases['active_cum'] = df_mas_cases['cases_active'].cumsum()

df_mas_deaths['total_vax'] = df_mas_deaths['deaths_pvax'] + df_mas_deaths['deaths_fvax']
df_mas_deaths['cum_deaths'] = df_mas_deaths['deaths_new_dod'].cumsum()
df_mas_deaths['cum_vax'] = df_mas_deaths['total_vax'].cumsum()

df_mas_monthly = df_mas_cases.append(df_mas_deaths)

def getYearMonth(s):
  return s.split("-")[1]+"-"+s.split("-")[0]
df_mas_monthly['YearMonth']= df_mas_monthly['date'].apply(lambda x: getYearMonth(x))

df_mas_monthly = df_mas_monthly.groupby('YearMonth').sum()
df_mas_monthly = df_mas_monthly.reset_index()
df_mas_monthly['vax_total_cases'] = df_mas_monthly['cases_fvax']+df_mas_monthly['cases_pvax']
df_mas_monthly['vax_total_deaths'] = df_mas_monthly['deaths_fvax']+df_mas_monthly['deaths_pvax']

df_mas_yearly = df_mas_cases.append(df_mas_deaths)

def getYear(s):
  return s.split("-")[0]

def getMonth(s):
  return s.split("-")[1]

df_mas_yearly['Year']= df_mas_yearly['date'].apply(lambda x: getYear(x))
df_mas_yearly['Month']= df_mas_yearly['date'].apply(lambda x: getMonth(x))

df_mas_yearly = df_mas_yearly.groupby('Year').sum().reset_index()
df_mas_yearly['vax_total_cases'] = df_mas_yearly['cases_fvax']+df_mas_yearly['cases_pvax']
df_mas_yearly['vax_total_deaths'] = df_mas_yearly['deaths_fvax']+df_mas_yearly['deaths_pvax']

fig = make_subplots(rows=1, cols=2, shared_xaxes=True, vertical_spacing=0.08,
                   subplot_titles=('<b>Yearly Total Covid19 Cases & Recovered</b>',
                                   '<b>Monthly Total Covid19 Cases & Recovered</b>'))
#Graph 1
fig.append_trace(go.Bar(x = df_mas_yearly['Year'], y = df_mas_yearly['cases_new'],
                        name='Cases Positive',marker_color='indianred'),row=1, col=1)
fig.append_trace(go.Bar(x = df_mas_yearly['Year'], y = df_mas_yearly['cases_recovered'],
                        name='Cases Recovered',marker_color='lightsalmon'),row=1, col=1)
#Graph 2
fig.append_trace(go.Bar(x = df_mas_monthly['YearMonth'], y = df_mas_monthly['cases_new'],
                        name='Cases Positive',marker_color='indianred'),row=1, col=2)
fig.append_trace(go.Bar(x = df_mas_monthly['YearMonth'], y = df_mas_monthly['cases_recovered'],
                        name='Cases Recovered',marker_color='lightsalmon'),row=1, col=2)

#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=400,showlegend=False)
#fig.update_layout(height=400,showlegend=False,title_text='Yearly and Monthly Total New And Recovered Cases', title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')

#Plotting the graph
fig.show()

fig = make_subplots(shared_xaxes=True, specs=[[{'secondary_y': True}]])

#Graph 1
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cases_new'], name ='Positive Cases', 
               line = dict(color='blue', width=1)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cases_recovered'], name = 'Recovered Cases',
               line = dict(color='red', width=1)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['total_vax'], name = 'Vax. Cases',
               line = dict(color='green', width=1)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_new'], name ='Cumulative Cases', 
               line = dict(color='blue', width=1)), secondary_y=False, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_recovered'], name = 'Cumulative Recovered',
               line = dict(color='red', width=1)), secondary_y=False, row=1, col=1)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_vax'], name = 'Cumulative Vax.',
               line = dict(color='green', width=1)), secondary_y=False, row=1, col=1)

fig.update_layout(title_text='Malaysia Covid19 New And Recovered Cases', title_x=0.5, showlegend=False,
                 height=600)
fig.update_xaxes(title_text='')
fig.update_annotations(font=dict(family="Helvetica", size=12))
fig.update_layout(font=dict(family="Helvetica", size=14))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", 
              x0='2021-10-11', x1='2021-10-11',  y0=0, y1=1000000)
fig.add_annotation(text='90% Vax.', x='2021-10-11', y=1000000, arrowhead=3, align='center', 
                   arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-90, showarrow=True, 
                   xanchor="left", yanchor="bottom")
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", 
              x0='2020-09-26', x1='2020-09-26',  y0=0, y1=500000)
fig.add_annotation(text='PRN Sabah', x='2020-09-26', y=500000, arrowhead=3, align='center', 
                   arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True, 
                   xanchor="left", yanchor="bottom")
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", 
              x0='2021-11-20', x1='2021-11-20',  y0=0, y1=500000)
fig.add_annotation(text='PRN Melaka', x='2021-11-20', y=500000, arrowhead=3, align='center', 
                   arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True, 
                   xanchor="left", yanchor="bottom")
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", 
              x0='2021-12-06', x1='2021-12-06',  y0=0, y1=400000)
fig.add_annotation(text='PRN Sarawak', x='2021-12-06', y=400000, arrowhead=3, align='center', 
                   arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True, 
                   xanchor="left", yanchor="bottom")
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", 
              x0='2021-01-12', x1='2021-01-12',  y0=0, y1=600000)
fig.add_annotation(text='Darurat Mula', x='2021-01-12', y=600000, arrowhead=3, align='center', 
                   arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True, 
                   xanchor="left", yanchor="bottom")
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", 
              x0='2021-08-01', x1='2021-08-01',  y0=0, y1=1100000)
fig.add_annotation(text='Darurat Tamat', x='2021-08-01', y=1100000, arrowhead=3, align='center', 
                   arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-30, showarrow=True, 
                   xanchor="left", yanchor="bottom")
fig.show()

df_2020 = df_mas_cases.query("date >= '2020-01-01' \
                            and date <= '2020-12-31'")
df_2021 = df_mas_cases.query("date >= '2021-01-01' \
                            and date <= '2021-12-31'")
df_2022 = df_mas_cases.query("date >= '2022-01-01' \
                            and date <= '2022-12-31'")

df_2020['cum_2020'] = df_2020['cases_new'].cumsum()
df_2021['cum_2021'] = df_2021['cases_new'].cumsum()
df_2022['cum_2022'] = df_2022['cases_new'].cumsum()

fig = make_subplots(rows=1, cols=3, specs=[[{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}]],
                    
subplot_titles=('<b>Year 2020</b>',
                '<b>Year 2021</b>',
                '<b>Year 2022</b>'))

fig.add_trace(go.Scatter(x = df_2020['date'], y = df_2020['cases_new'], name = 'Daily Cases',
               line = dict(color='green', width=1)), secondary_y=False, row=1, col=1)
fig.add_trace(go.Scatter(x = df_2020['date'], y = df_2020['cum_2020'], name = 'Cumulative Cases',
               line = dict(color='green', width=1.5)), secondary_y=True, row=1, col=1)
fig.add_trace(go.Scatter(x = df_2020['date'], y = df_2020['cases_active'], name = 'Active Cases',
               line = dict(color='green', width=1.5)), secondary_y=True, row=1, col=1)

fig.add_trace(go.Scatter(x = df_2021['date'], y = df_2021['cases_new'], name = 'Cases Daily',
               line = dict(color='blue', width=1)), secondary_y=False, row=1, col=2)
fig.add_trace(go.Scatter(x = df_2021['date'], y = df_2021['cum_2021'], name = 'Cases',
               line = dict(color='blue', width=1.5)), secondary_y=True, row=1, col=2)
fig.add_trace(go.Scatter(x = df_2021['date'], y = df_2021['cases_active'], name = 'Active Cases',
               line = dict(color='blue', width=1.5)), secondary_y=True, row=1, col=2)

fig.add_trace(go.Scatter(x = df_2022['date'], y = df_2022['cases_new'], name = 'Daily Cases',
               line = dict(color='red', width=1)), secondary_y=False, row=1, col=3)
fig.add_trace(go.Scatter(x = df_2022['date'], y = df_2022['cum_2022'], name = 'Cumulative Cases',
               line = dict(color='red', width=1.5)), secondary_y=True, row=1, col=3)
fig.add_trace(go.Scatter(x = df_2022['date'], y = df_2022['cases_active'], name = 'Active Cases',
               line = dict(color='red', width=1.5)), secondary_y=True, row=1, col=3)

fig.update_layout(title_text='Malaysia Covid19 Cases By Year', title_x=0.5, showlegend=False,
                 height=350)
fig.update_xaxes(title_text='')
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=350,showlegend=False)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')

#Plotting the graph
fig.show()

#Creating the main key points from the datasets
print('Key Points Highlights:-')
print('-----------------------------------------------------------------')
print('1. Total Positive Cases                  :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Cases Positive']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Cases (%)']),'%')
print('2. Total Positive Cases After Vaccinated :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Cases Vax.']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Cases Vax. (%)']),'%')
print('3. Total Recovered Cases                 :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Cases Recovered']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Cases Recovered (%)']),'%')
print('4. Total Death Cases                     :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Death Cases']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Deaths (%)']),'%')
print('5. Total Death Cases After Vaccinated    :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'Deaths Vax.']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Deaths Vax. (%)']),'%')
print('6. Total 1 Dose Vaccinated               :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'daily_partial']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Vax. 1D (%)']),'%')
print('7. Total 2 Dose Vaccinated               :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'daily_full']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Vax. 2D (%)']),'%')
print('8. Total 3 Dose Vaccinated               :','{0:,.0f}'.format(df_states_table.at[df_states_table.index[16],'daily_booster']),'/','{0:.1f}'.format(df_states_table.at[df_states_table.index[16],'Vax. 3D (%)']),'%')
print('-----------------------------------------------------------------')

Key Points Highlights:-
-----------------------------------------------------------------
1. Total Positive Cases                  : 3,974,019 / 12.2 %
2. Total Positive Cases After Vaccinated : 1,562,005 / 39.3 %
3. Total Recovered Cases                 : 3,656,415 / 92.0 %
4. Total Death Cases                     : 34,328 / 0.9 %
5. Total Death Cases After Vaccinated    : 12,236 / 35.6 %
6. Total 1 Dose Vaccinated               : 27,203,826 / 83.3 %
7. Total 2 Dose Vaccinated               : 25,785,542 / 79.0 %
8. Total 3 Dose Vaccinated               : 15,465,855 / 47.4 %
-----------------------------------------------------------------

df_states_table.drop(['daily_partial','daily_full','pop','daily','daily_booster'],axis='columns',inplace=True)

df_states_table.drop(['Cases Vax.','Death BID','Deaths Vax.'],axis='columns',inplace=True)

df_states_table.style.set_caption("Malaysia Details Of Covid19 Cases At Each States").set_table_styles([{
    'selector': 'caption',
    'props': [
        ('color', 'black'),
        ('font-size', '26px'),
        ("text-align", "center"),
        ('text-decoration', 'underline'),
        ('font-family','Arial'),
        ('text-shadow', '2px 2px 5px grey')
    ]},(dict
        (selector='th',props=[('text-align',
                               'left')]))]).format(
    {'Cases Positive':'{:,.0f}','Cases Recovered':'{:,.0f}','Cases Vax.':'{:,.0f}','Death Cases':'{:,.0f}',
     'Deaths (%)':'{:,.1f}','Deaths BID':'{:,.0f}','Deaths Vax.':'{:,.0f}','Cases (%)':'{:,.1f}',
     'Cases Recovered (%)':'{:,.1f}','Cases Vax. (%)':'{:,.1f}','Vax. 2D (%)':'{:,.1f}','Vax. 1D (%)':'{:,.1f}',
     'Deaths Vax. (%)':'{:,.1f}','Deaths BID (%)':'{:,.1f}','Vax. 3D (%)':'{:,.1f}'}
).set_properties(subset=['States'],**{'text-align': 'left'}).apply(
    lambda x: ['background: salmon' if x.name in ['Total'] else '' for i in x], axis=1).hide_index()

Malaysia Details Of Covid19 Cases At Each States

States	Cases Positive	Cases Recovered	Death Cases	Cases (%)	Cases Recovered (%)	Cases Vax. (%)	Deaths (%)	Deaths Vax. (%)	Deaths BID (%)	Vax. 3D (%)	Vax. 2D (%)	Vax. 1D (%)
Johor	360,932	337,291	4,324	9.5	93.5	37.5	1.2	39.2	13.2	53.8	80.7	86.0
Kedah	280,459	254,425	2,482	12.8	90.7	41.1	0.9	35.6	16.8	32.2	72.1	75.0
Kelantan	240,043	228,305	1,388	12.4	95.1	46.5	0.6	35.8	26.7	16.0	61.3	63.4
Melaka	110,983	100,863	1,060	11.8	90.9	36.6	1.0	38.2	13.9	56.1	76.8	82.1
Negeri Sembilan	178,785	158,415	1,417	15.8	88.6	31.4	0.8	30.1	12.4	56.4	84.5	89.1
Pahang	157,886	143,016	915	9.4	90.6	42.6	0.6	36.4	13.8	35.2	70.1	73.5
Perak	177,495	162,666	1,718	7.1	91.6	39.5	1.0	44.5	14.7	44.3	74.4	78.3
Perlis	15,820	13,814	169	6.2	87.3	58.1	1.1	45.6	4.7	29.5	80.3	83.7
Pulau Pinang	254,266	237,014	1,903	14.3	93.2	42.2	0.7	40.2	21.2	58.2	86.3	90.7
Sabah	363,570	336,362	3,092	9.5	92.5	41.2	0.9	25.5	40.6	22.0	62.8	63.2
Sarawak	286,995	269,578	1,654	10.2	93.9	50.0	0.6	48.4	21.2	54.5	75.8	84.4
Selangor	1,087,150	1,000,759	10,435	16.6	92.1	35.9	1.0	33.1	21.6	59.5	73.3	77.8
Terengganu	115,920	106,934	825	9.1	92.2	45.6	0.7	42.9	12.6	26.2	69.3	71.5
W.P. Kuala Lumpur	306,575	272,682	2,762	17.6	88.9	33.0	0.9	34.8	26.0	96.8	173.9	183.8
W.P. Labuan	20,082	19,115	152	20.1	95.2	24.4	0.8	9.2	28.3	47.0	79.9	87.6
W.P. Putrajaya	17,058	15,176	32	14.7	89.0	41.0	0.2	40.6	3.1	67.9	127.2	136.8
Malaysia	3,974,019	3,656,415	34,328	12.2	92.0	39.3	0.9	35.6	21.0	47.4	79.0	83.3

df_states_graph = df_states
df_states_graph['vaksin_percentage_partial'] = df_states_graph['daily_partial']/df_states_graph['pop']*100
df_states_graph['vaksin_percentage_full'] = df_states_graph['daily_full']/df_states_graph['pop']*100
df_states_graph['deaths_vax_total'] = df_states_graph['deaths_pvax'] + df_states_graph['deaths_fvax']
df_states_graph['cases_vax_total'] = df_states_graph['cases_pvax'] + df_states_graph['cases_fvax']

df_mas_deaths['deaths_vax_total'] = df_mas_deaths['deaths_pvax'] + df_mas_deaths['deaths_fvax']
df_mas['deaths_vax_total'] = df_mas['deaths_pvax'] + df_mas['deaths_fvax']
df_mas_test['total_test'] = df_mas_test['rtk-ag'] + df_mas_test['pcr']
df_mas_test['cum_total'] = df_mas_test['total_test'].cumsum()

df_mas_new = df_mas[['date','cases_new','deaths_new_dod']]
df_mas_new['cases_new_7'] = df_mas_new['cases_new'].shift(7)
df_mas_new['cases_new_14'] = df_mas_new['cases_new'].shift(14)
df_mas_new['cases_new_21'] = df_mas_new['cases_new'].shift(21)
df_mas_new['deaths_new_7'] = df_mas_new['deaths_new_dod'].shift(7)
df_mas_new['deaths_new_14'] = df_mas_new['deaths_new_dod'].shift(14)
df_mas_new['deaths_new_21'] = df_mas_new['deaths_new_dod'].shift(21)
df_mas_new = df_mas_new.tail(7)
df_mas_new['cum_new'] = df_mas_new['cases_new'].cumsum()
df_mas_new['cum_new_7'] = df_mas_new['cases_new_7'].cumsum()
df_mas_new['cum_new_14'] = df_mas_new['cases_new_14'].cumsum()
df_mas_new['cum_new_21'] = df_mas_new['cases_new_21'].cumsum()
df_mas_new['cum_deaths_new'] = df_mas_new['deaths_new_dod'].cumsum()
df_mas_new['cum_deaths_7'] = df_mas_new['deaths_new_7'].cumsum()
df_mas_new['cum_deaths_14'] = df_mas_new['deaths_new_14'].cumsum()
df_mas_new['cum_deaths_21'] = df_mas_new['deaths_new_21'].cumsum()

print('Four Weeks Cumulative Daily Cases Comparison Analysis:-')
print('--------------------------------------------')
print('Average Week 1: ', '{0:,.0f}'.format(df_mas_new['cum_new'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cum_new'].mean()-df_mas_new['cum_new_7'].mean()),'diff.)')
print('Average Week 2: ', '{0:,.0f}'.format(df_mas_new['cum_new_7'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cum_new_7'].mean()-df_mas_new['cum_new_14'].mean()),'diff.)')
print('Average Week 3: ', '{0:,.0f}'.format(df_mas_new['cum_new_14'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cum_new_14'].mean()-df_mas_new['cum_new_21'].mean()),'diff.)')
print('Average Week 4: ', '{0:,.0f}'.format(df_mas_new['cum_new_21'].mean()), '-')
print('--------------------------------------------')
print('Four Weeks Daily Cases Comparison Analysis:-')
print('--------------------------------------------')
print('Average Week 1:', '{0:,.0f}'.format(df_mas_new['cases_new'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cases_new'].mean()-df_mas_new['cases_new_7'].mean()),'diff.)')
print('Average Week 2:', '{0:,.0f}'.format(df_mas_new['cases_new_7'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cases_new_7'].mean()-df_mas_new['cases_new_14'].mean()),'diff.)')
print('Average Week 3:', '{0:,.0f}'.format(df_mas_new['cases_new_14'].mean()), '(', '{0:,.0f}'.format(df_mas_new['cases_new_14'].mean()-df_mas_new['cases_new_21'].mean()),'diff.)')
print('Average Week 4:', '{0:,.0f}'.format(df_mas_new['cases_new_21'].mean()), '-')
print('--------------------------------------------')

Four Weeks Cumulative Daily Cases Comparison Analysis:-
--------------------------------------------
Average Week 1:  98,232 ( -18,315 diff.)
Average Week 2:  116,547 ( 9,924 diff.)
Average Week 3:  106,622 ( -5,364 diff.)
Average Week 4:  111,987 -
--------------------------------------------
Four Weeks Daily Cases Comparison Analysis:-
--------------------------------------------
Average Week 1: 24,712 ( -4,697 diff.)
Average Week 2: 29,409 ( 837 diff.)
Average Week 3: 28,572 ( -46 diff.)
Average Week 4: 28,617 -
--------------------------------------------

fig = make_subplots(rows=1, cols=4, shared_xaxes=True, vertical_spacing=0.08,
                   subplot_titles=('<b>New Confirmed Cases Daily</b>',
                                   '<b>New Confirmed Cases Cumulative</b>',
                                   '<b>New Deaths Cases Daily</b>',
                                   '<b>New Deaths Cases Cumulative</b>'))
#Graph 1
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cases_new'],name='Week 1', mode="lines",
                           line = dict(color='red',width=0.5)),row=1, col=1)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cases_new_7'],name='Week 2', mode="lines",
                           line = dict(color='blue',width=0.5)),row=1, col=1)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cases_new_14'],name='Week 3', mode="lines",
                           line = dict(color='green',width=0.5)),row=1, col=1)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cases_new_21'],name='Week 4', mode="lines",
                           line = dict(color='purple',width=0.5)),row=1, col=1)
fig.add_hline(y=df_mas_new['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Ave. Week 1", annotation_position="bottom left",row=1, col=1)
fig.add_hline(y=df_mas_new['cases_new_7'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Week 2", annotation_position="bottom right",row=1, col=1)
fig.add_hline(y=df_mas_new['cases_new_14'].mean(), line_dash="dot",line_color="green",
              annotation_text="Ave. Week 3", annotation_position="bottom left",row=1, col=1)
fig.add_hline(y=df_mas_new['cases_new_21'].mean(), line_dash="dot",line_color="purple",
              annotation_text="Ave. Week 4", annotation_position="bottom right",row=1, col=1)
#Graph 2
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_new'],name='Week 1', mode="lines",
                           line = dict(color='red',width=0.5)),row=1, col=2)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_new_7'],name='Week 2', mode="lines",
                           line = dict(color='blue',width=0.5)),row=1, col=2)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_new_14'],name='Week 3', mode="lines",
                           line = dict(color='green',width=0.5)),row=1, col=2)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_new_21'],name='Week 4', mode="lines",
                           line = dict(color='purple',width=0.5)),row=1, col=2)
fig.add_hline(y=df_mas_new['cum_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Ave. Week 1", annotation_position="bottom left",row=1, col=2)
fig.add_hline(y=df_mas_new['cum_new_7'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Week 2", annotation_position="bottom right",row=1, col=2)
fig.add_hline(y=df_mas_new['cum_new_14'].mean(), line_dash="dot",line_color="green",
              annotation_text="Ave. Week 3", annotation_position="top left",row=1, col=2)
fig.add_hline(y=df_mas_new['cum_new_21'].mean(), line_dash="dot",line_color="purple",
              annotation_text="Ave. Week 4", annotation_position="bottom right",row=1, col=2)
#Graph 3
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['deaths_new_dod'],name='Week 1', mode="lines",
                           line = dict(color='red', width=0.5)),row=1, col=3)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['deaths_new_7'],name='Week 2', mode="lines",
                           line = dict(color='blue', width=0.5)),row=1, col=3)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['deaths_new_14'],name='Week 3', mode="lines",
                           line = dict(color='green', width=0.5)),row=1, col=3)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['deaths_new_21'],name='Week 4', mode="lines",
                           line = dict(color='purple', width=0.5)),row=1, col=3)
fig.add_hline(y=df_mas_new['deaths_new_dod'].mean(), line_dash="dot",line_color="red",
              annotation_text="Ave. Week 1", annotation_position="bottom left",row=1, col=3)
fig.add_hline(y=df_mas_new['deaths_new_7'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Week 2", annotation_position="bottom right",row=1, col=3)
fig.add_hline(y=df_mas_new['deaths_new_14'].mean(), line_dash="dot",line_color="green",
              annotation_text="Ave. Week 3", annotation_position="bottom left",row=1, col=3)
fig.add_hline(y=df_mas_new['deaths_new_21'].mean(), line_dash="dot",line_color="purple",
              annotation_text="Ave. Week 4", annotation_position="bottom right",row=1, col=3)
#Graph 4
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_deaths_new'],name='Week 1', mode="lines",
                           line = dict(color='red', width=0.5)),row=1, col=4)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_deaths_7'],name='Week 2', mode="lines",
                           line = dict(color='blue', width=0.5)),row=1, col=4)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_deaths_14'],name='Week 3', mode="lines",
                           line = dict(color='green', width=0.5)),row=1, col=4)
fig.append_trace(go.Scatter(x = df_mas_new['date'], y = df_mas_new['cum_deaths_21'],name='Week 4', mode="lines",
                           line = dict(color='purple', width=0.5)),row=1, col=4)
fig.add_hline(y=df_mas_new['cum_deaths_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Ave. Week 1", annotation_position="bottom left",row=1, col=4)
fig.add_hline(y=df_mas_new['cum_deaths_7'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Week 2", annotation_position="bottom right",row=1, col=4)
fig.add_hline(y=df_mas_new['cum_deaths_14'].mean(), line_dash="dot",line_color="green",
              annotation_text="Ave. Week 3", annotation_position="bottom left",row=1, col=4)
fig.add_hline(y=df_mas_new['cum_deaths_21'].mean(), line_dash="dot",line_color="purple",
              annotation_text="Ave. Week 4", annotation_position="top right",row=1, col=4)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=400,showlegend=False,title_text="Malaysia Covid19 Latest Cases In Details (4 Week Comparison)", title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black',
                visible=True, showticklabels=False)
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()

df_mas_cases['cum_new'] = df_mas_cases['cases_new'].cumsum()
df_mas_cases['cum_recovered'] = df_mas_cases['cases_recovered'].cumsum()
df_mas_deaths['cum_deaths'] = df_mas_deaths['deaths_new_dod'].cumsum()
df_mas_deaths['cum_deaths_vax'] = df_mas_deaths['deaths_vax_total'].cumsum()

fig = make_subplots(rows=2, cols=4, shared_xaxes=True, vertical_spacing=0.08,
                    specs=[[{'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}], 
                           [{'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}]],

subplot_titles=('<b>Cases: Total VS Recover</b>',
                '<b>ICU Daily Admitted</b>',
                '<b>Hosp.: Admit VS Discharge</b>',
                '<b>PKRC: Admitted VS Discharged</b>',
                '<b>Deaths: Total, BID & Vax.</b>',
                '<b>Total Daily Covid19 Test</b>',
                '<b>States Hosp. Admission & Discharge</b>',
                '<b>States Vax. (%): 1D VS 2D</b>'))

#Graph 1
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cases_new'], name='Positive Cases',
                           line = dict(color='red',width=0.5)), row=1, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_new'], name='Positive Cases',
                           line = dict(color='red',width=1)), row=1, col=1, secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cases_recovered'],name='Recovered Cases',
                            line = dict(color='blue',width=0.5)),row=1, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_cases['date'], y = df_mas_cases['cum_recovered'],name='Recovered Cases',
                            line = dict(color='blue',width=1)),row=1, col=1, secondary_y=False)
fig.add_hline(y=df_mas_cases['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Ave. Cases", annotation_position="bottom left",row=1, col=1)
fig.add_hline(y=df_mas_cases['cases_recovered'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Recovered", annotation_position="top left",row=1, col=1)
#Graph 2
fig.add_trace(go.Scatter(x = df_icu_daily['date'], y = df_icu_daily['icu_covid'],name='ICU Covid',
                           line = dict(color='blue', width=0.5)),row=1, col=2, secondary_y=True)
fig.add_trace(go.Scatter(x = df_icu_daily['date'], y = df_icu_daily['icu_covid_cum'],name='ICU Covid',
                           line = dict(color='red', width=0.5)),row=1, col=2, secondary_y=False)
fig.add_hline(y = df_icu_daily['icu_covid'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Daily", annotation_position="bottom left",row=1, col=2)
fig.add_hline(y = df_icu_daily['icu_covid_cum'].mean(), line_dash="dot",line_color="red",
              annotation_text="Ave. Cum.", annotation_position="bottom left",row=1, col=2)
# Graph 3
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['deaths_new_dod'],name='Deaths Cases',
                           line = dict(width=0.5, color='blue')),row=2, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['cum_deaths'],name='Deaths Cases',
                           line = dict(width=1, color='blue')),row=2, col=1, secondary_y=False)
fig.add_hline(y=df_mas_deaths['deaths_new_dod'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Daily Deaths", annotation_position="top left",row=2, col=1)
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['deaths_bid_dod'],name='BID Cases',
                           line = dict(width=0.5, color='red')),row=2, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['deaths_vax_total'],name='Deaths Vaccinated Cases',
                            line = dict(width=0.5, color='black')),row=2, col=1, secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_deaths['date'], y = df_mas_deaths['cum_deaths_vax'],name='Deaths Vaccinated Cases',
                            line = dict(width=1, color='black')),row=2, col=1, secondary_y=False)
#Graph 4
fig.add_trace(go.Scatter(x = df_mas_test['date'], y = df_mas_test['total_test'],name='Daily Covid19 Test',
                           line = dict(color='blue', width=0.5)),row=2, col=2, secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_test['date'], y = df_mas_test['cum_total'],name='Cumulative Covid19 Test',
                           line = dict(color='red', width=1)),row=2, col=2, secondary_y=True)
fig.add_hline(y=df_mas_test['total_test'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Daily Test", annotation_position="top left",row=2, col=2)
#Graph 5
fig.add_trace(go.Scatter(x = df_hosp_daily['date'], y = df_hosp_daily['admitted_covid'],name='Admitted',
                           line = dict(color='blue', width=0.5)),row=1, col=3, secondary_y=True)
fig.add_trace(go.Scatter(x = df_hosp_daily['date'], y = df_hosp_daily['discharged_covid'],name='Discharged',
                           line = dict(color='red', width=0.5)),row=1, col=3, secondary_y=True)
fig.add_trace(go.Scatter(x = df_hosp_daily['date'], y = df_hosp_daily['admitted_covid_cum'],name='Admitted',
                           line = dict(color='blue', width=1)),row=1, col=3, secondary_y=False)
fig.add_trace(go.Scatter(x = df_hosp_daily['date'], y = df_hosp_daily['discharged_covid_cum'],name='Discharged',
                           line = dict(color='red', width=1)),row=1, col=3, secondary_y=False)
fig.add_hline(y = df_hosp_daily['admitted_covid'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Admitted", annotation_position="bottom left",row=1, col=3)
fig.add_hline(y = df_hosp_daily['discharged_covid'].mean(), line_dash="dot",line_color="red",
              annotation_text="Ave. Discharged", annotation_position="top left",row=1, col=3)
#Graph 6
fig.add_trace(go.Scatter(x = df_pkrc_daily['date'], y = df_pkrc_daily['admitted_covid'],name='Admitted',
                           line = dict(color='blue', width=0.5)),row=1, col=4, secondary_y=True)
fig.add_trace(go.Scatter(x = df_pkrc_daily['date'], y = df_pkrc_daily['discharged_covid'],name='Discharged',
                           line = dict(color='red', width=0.5)),row=1, col=4, secondary_y=True)
fig.add_trace(go.Scatter(x = df_pkrc_daily['date'], y = df_pkrc_daily['admitted_covid_cum'],name='Admitted',
                           line = dict(color='blue', width=1)),row=1, col=4, secondary_y=False)
fig.add_trace(go.Scatter(x = df_pkrc_daily['date'], y = df_pkrc_daily['discharged_covid_cum'],name='Discharged',
                           line = dict(color='red', width=1)),row=1, col=4, secondary_y=False)
fig.add_hline(y = df_pkrc_daily['admitted_covid'].mean(), line_dash="dot",line_color="blue",
              annotation_text="Ave. Admitted", annotation_position="bottom left",row=1, col=4)
fig.add_hline(y = df_pkrc_daily['discharged_covid'].mean(), line_dash="dot",line_color="red",
              annotation_text="Ave. Discharged", annotation_position="top left",row=1, col=4)
#Graph 7
fig.add_trace(go.Bar(x = df_states_graph['state'], y = df_states_graph['admitted_covid'],name='Admitted Cases',
                       marker_color='blue'),row=2, col=3, secondary_y=False)
fig.add_trace(go.Bar(x = df_states_graph['state'], y = df_states_graph['discharged_covid'],name='Discharged Cases',
                       marker_color='red'),row=2, col=3, secondary_y=False)
#Graph 8
fig.add_trace(go.Bar(x = df_states_graph['state'], y = df_states_graph['vaksin_percentage_partial'],
                        name='Partial Vaksin (%)',marker_color='blue'),row=2, col=4, secondary_y=False)
fig.add_trace(go.Bar(x = df_states_graph['state'], y = df_states_graph['vaksin_percentage_full'],
                        name='Full Vaksin (%)',marker_color='red'),row=2, col=4, secondary_y=False)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=600,showlegend=False,title_text="Malaysia Covid19 Cases Overview", title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')

# add a horizontal line & annotations
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0=0, x1=16, xref="paper", y0=90, y1=90, yref="y",row=2, col=4)
fig.add_annotation(text='90% Vax. Pop.', x=6, y=90, arrowhead=1, showarrow=True,row=2, col=4)
#Plotting the graph
fig.show()

fig = make_subplots(rows=1, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}]],
                    
subplot_titles=('<b>Total Daily Vax.</b>',
                '<b>Cumulative Daily Vax.</b>',
                '<b>Full, Partial & Booster</b>',
                '<b>Pfizer, Sinovac, AZ & Cansino</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['daily'],name='Total Daily Vaccinated',
                           line = dict(color='red',width=0.5)),row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Vax. (%)'],name='Vax. (%)',
                           line = dict(color='blue',width=0.5)),row=1, col=1,secondary_y=True)
fig.add_hline(y=df_mas_vaksin['daily'].mean(), line_dash="dot",line_color="black",
              annotation_text="Ave. Daily Vax.", annotation_position="top left",row=1, col=1)
#Graph 2
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Cum. Daily'],name='Cum. Daily Vaccinated',
                           line = dict(color='red',width=0.5)),row=1, col=2,secondary_y=False)
fig.add_hline(y=df_mas_vaksin['Cum. Daily'].mean(), line_dash="dot",line_color="black",
              annotation_text="Ave. Cum. Vax.", annotation_position="top left",row=1, col=2)
#Grpah 3
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['daily_partial'],name='Partial',
                           line = dict(color='red',width=0.5)),row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['daily_full'],name='Full',
                           line = dict(color='blue',width=0.5)),row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['daily_booster'],name='Booster',
                           line = dict(color='green',width=0.5)),row=1, col=3,secondary_y=True)
fig.add_hline(y=df_mas_vaksin['daily'].mean(), line_dash="dot",line_color="black",
              annotation_text="Ave. Daily Vax.", annotation_position="top left",row=1, col=3)
#Graph 4
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Total Pfizer'],name='Pfizer',
                           line = dict(color='red',width=0.5)),row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Total Sinovac'],name='Sinovac',
                           line = dict(color='blue',width=0.5)),row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['Total AstraZ'],name='Astra Zaneca',
                           line = dict(color='green',width=0.5)),row=1, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_mas_vaksin['date'], y = df_mas_vaksin['cansino'],name='Cansino',
                           line = dict(color='black',width=0.5)),row=1, col=4,secondary_y=True)
fig.add_hline(y=df_mas_vaksin['daily'].mean(), line_dash="dot",line_color="black",
              annotation_text="Ave. Daily Vax.", annotation_position="top left",row=1, col=4)
#Update Fonts & Size
fig.update_annotations(font=dict(family="Helvetica", size=11))
fig.update_layout(font=dict(family="Helvetica", size=11))
fig.update_layout(height=350,showlegend=False,title_text="Daily Vaccination Details", title_x=0.5)
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')

#Plotting the graph
fig.show()

#Forecasting of New Cases VS Recovered Cases
#Importing the required module
#from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
#from statsmodels.tsa.statespace.varmax import VARMAX
#from statsmodels.tsa.api import VAR
#from statsmodels.tsa.stattools import grangercausalitytests, adfuller
#from tqdm import tqdm_notebook
#from itertools import product
#import statsmodels.api as sm
#import warnings
#warnings.filterwarnings('ignore')
#Importing the required datasets
#filepath_cases = 'https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/cases_malaysia.csv'
#filepath_deaths = "https://raw.githubusercontent.com/MoH-Malaysia/covid19-public/main/epidemic/deaths_malaysia.csv"
#filepath_vaksin = 'https://raw.githubusercontent.com/CITF-Malaysia/citf-public/main/vaccination/vax_malaysia.csv'
#Preparing the required datasets
#filepath_cases = pd.read_csv(filepath_cases, parse_dates=['date'], index_col='date').astype('float64')
#filepath_deaths = pd.read_csv(filepath_deaths, parse_dates=['date'], index_col='date').astype('float64')
#filepath_vaksin = pd.read_csv(filepath_vaksin, parse_dates=['date'], index_col='date').astype('float64')
#macro_data = pd.merge(filepath_cases,filepath_deaths,on='date')
#macro_data = pd.merge(macro_data,filepath_vaksin,on='date')
#macro_data['cases_vax_total'] = macro_data['cases_pvax'] + macro_data['cases_fvax']
#macro_data['deaths_vax_total'] = macro_data['deaths_pvax'] + macro_data['deaths_fvax']
#macro_data = macro_data[['cases_new','cases_active']]
#train_df=macro_data[:-12]
#test_df=macro_data[-360:]
#model = VAR(train_df.diff()[1:])
#sorted_order=model.select_order(maxlags=30)
#var_model = VARMAX(train_df, order=(4,0),enforce_stationarity= False)
#fitted_model = var_model.fit(disp=True)
#Creating the required forecast
#n_forecast = 180
#predict = fitted_model.get_prediction(start=len(train_df),end=len(train_df) + n_forecast-1)
#predictions=predict.predicted_mean
#predictions.columns=['cases_predicted','cases_active_predicted']
#test_vs_pred=pd.concat([test_df,predictions],axis=1)
#test_vs_pred = test_vs_pred.reset_index()
#test_vs_pred['cases_new_cum'] = test_vs_pred['cases_new'].cumsum()
#test_vs_pred['cases_active_cum'] = test_vs_pred['cases_active'].cumsum()
#test_vs_pred['cases_predicted_cum'] = test_vs_pred['cases_predicted'].cumsum()
#test_vs_pred['cases_active_predicted_cum'] = test_vs_pred['cases_active_predicted'].cumsum()
#Plotting the required forecast
#fig = make_subplots(specs=[[{'secondary_y': True}]])
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_new'], name ='Positive Cases', line = dict(color='blue', width=1)), secondary_y=True)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_new_cum'], name ='Positive Cases', line = dict(color='blue', width=1)), secondary_y=False)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_active'], name = 'Positive Cases After Vaccinated',line = dict(color='red', width=1)), secondary_y=True)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_active_cum'], name = 'Positive Cases After Vaccinated',line = dict(color='red', width=1)), secondary_y=False)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_predicted'], name = 'Predicted Positive Cases',line = dict(color='blue', width=0.75)), secondary_y=True)
#fig.add_trace(go.Scatter(x = test_vs_pred['index'], y = test_vs_pred['cases_active_predicted'], name = 'Predicted Positive Cases After Vaccinated',line = dict(color='red', width=0.75)), secondary_y=True)


#fig.update_layout(title_text='Malaysia Covid19 Forecast of Positive & Active Cases (Based on VAR Model)', title_x=0.5,showlegend=False, height=600)
#fig.update_xaxes(title_text='')
#fig.update_annotations(font=dict(family="Helvetica", size=12))
#fig.update_layout(font=dict(family="Helvetica", size=14))
#fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", x0='2021-10-11', x1='2021-10-11',  y0=0, y1=800000)
#fig.add_annotation(text='90% Adult Vax.', x='2021-10-11', y=800000, arrowhead=3, align='center', arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-90, showarrow=True, xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", x0='2021-11-20', x1='2021-11-20',  y0=0, y1=600000)
#fig.add_annotation(text='PRN Melaka', x='2021-11-20', y=600000, arrowhead=3, align='center', arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-90, showarrow=True, xanchor="left", yanchor="bottom")
#fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot", x0='2021-12-06', x1='2021-12-06',  y0=0, y1=500000)
#fig.add_annotation(text='PRN Sarawak', x='2021-12-06', y=500000, arrowhead=3, align='center', arrowsize=1, arrowwidth=2, arrowcolor="#636363",xref='x', ax=50, ay=-90, showarrow=True, xanchor="left", yanchor="bottom")
#fig.show()

df = df_states_cases
df_joh = df[df.state == 'Johor']
df_ked = df[df.state == 'Kedah']
df_kel = df[df.state == 'Kelantan']
df_mel = df[df.state == 'Melaka']
df_neg = df[df.state == 'Negeri Sembilan']
df_pah = df[df.state == 'Pahang']
df_prk = df[df.state == 'Perak']
df_per = df[df.state == 'Perlis']
df_pen = df[df.state == 'Pulau Pinang']
df_sab = df[df.state == 'Sabah']
df_sar = df[df.state == 'Sarawak']
df_sel = df[df.state == 'Selangor']
df_ter = df[df.state == 'Terengganu']
df_kul = df[df.state == 'W.P. Kuala Lumpur']
df_lab = df[df.state == 'W.P. Labuan']
df_put = df[df.state == 'W.P. Putrajaya']
fig = make_subplots(rows=4, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}]],
                    
subplot_titles=('<b>Johor</b>', '<b>Kedah</b>', '<b>Kelantan</b>', '<b>Melaka</b>',
                '<b>Negeri Sembilan</b>', '<b>Pahang</b>', '<b>Perak</b>', '<b>Perlis</b>',
                '<b>Pulau Pinang</b>', '<b>Sabah</b>', '<b>Sarawak</b>', '<b>Selangor</b>',
                '<b>Terengganu</b>', '<b>W.P. Kuala Lumpur</b>', '<b>W.P. Labuan</b>', '<b>W.P. Putrajaya</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=False)
fig.add_hline(y=df_joh['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=1, col=1)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=True)
#Graph 2
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=False)
fig.add_hline(y=df_ked['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=1, col=2)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=True)
#Graph 3
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=False)
fig.add_hline(y=df_kel['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=1, col=3)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=True)
#Graph 4
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=False)
fig.add_hline(y=df_mel['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=1, col=4)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=True)
#Graph 5
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=False)
fig.add_hline(y=df_neg['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=2, col=1)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=True)
#Graph 6
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=False)
fig.add_hline(y=df_pah['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=2, col=2)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=True)
#Graph 7
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=False)
fig.add_hline(y=df_prk['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=2, col=3)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=True)
#Graph 8
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=False)
fig.add_hline(y=df_per['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=2, col=4)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=True)
#Graph 9
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=3, col=1,secondary_y=False)
fig.add_hline(y=df_pen['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=3, col=1)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=3, col=1,secondary_y=True)
#Graph 10
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=3, col=2,secondary_y=False)
fig.add_hline(y=df_sab['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=3, col=2)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=3, col=2,secondary_y=True)
#Graph 11
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=3, col=3,secondary_y=False)
fig.add_hline(y=df_sar['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=3, col=3)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=3, col=3,secondary_y=True)
#Graph 12
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=3, col=4,secondary_y=False)
fig.add_hline(y=df_sel['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=3, col=4)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=3, col=4,secondary_y=True)
#Graph 13
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=4, col=1,secondary_y=False)
fig.add_hline(y=df_ter['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=4, col=1)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=4, col=1,secondary_y=True)
#Graph 14
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=4, col=2,secondary_y=False)
fig.add_hline(y=df_kul['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=4, col=2)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=4, col=2,secondary_y=True)
#Graph 15
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=4, col=3,secondary_y=False)
fig.add_hline(y=df_lab['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=4, col=3)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=4, col=3,secondary_y=True)
#Graph 16
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cases_new'],name='cases_new',
                        line = dict(color='red', width=0.75)), row=4, col=4,secondary_y=False)
fig.add_hline(y=df_put['cases_new'].mean(), line_dash="dot",line_color="red",
              annotation_text="Average Cases", annotation_position="top left",row=4, col=4)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cases_recovered'],name='cases_recovered',
                        line = dict(color='blue', width=0.75)), row=4, col=4,secondary_y=True)
#Adding title and adjusting the layout
fig.update_layout(height=1000,showlegend=False,title_text="Covid19 Cases & Recovered At Each States",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()

df = df_states_deaths
df['deaths_vax_total'] = df['deaths_fvax'] + df['deaths_pvax'] 
df_joh = df[df.state == 'Johor']
df_ked = df[df.state == 'Kedah']
df_kel = df[df.state == 'Kelantan']
df_mel = df[df.state == 'Melaka']
df_neg = df[df.state == 'Negeri Sembilan']
df_pah = df[df.state == 'Pahang']
df_prk = df[df.state == 'Perak']
df_per = df[df.state == 'Perlis']
df_pen = df[df.state == 'Pulau Pinang']
df_sab = df[df.state == 'Sabah']
df_sar = df[df.state == 'Sarawak']
df_sel = df[df.state == 'Selangor']
df_ter = df[df.state == 'Terengganu']
df_kul = df[df.state == 'W.P. Kuala Lumpur']
df_lab = df[df.state == 'W.P. Labuan']
df_put = df[df.state == 'W.P. Putrajaya']
fig = make_subplots(rows=4, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}]],
                    
subplot_titles=('<b>Johor</b>', '<b>Kedah</b>', '<b>Kelantan</b>', '<b>Melaka</b>',
                '<b>Negeri Sembilan</b>', '<b>Pahang</b>', '<b>Perak</b>', '<b>Perlis</b>',
                '<b>Pulau Pinang</b>', '<b>Sabah</b>', '<b>Sarawak</b>', '<b>Selangor</b>',
                '<b>Terengganu</b>', '<b>W.P. Kuala Lumpur</b>', '<b>W.P. Labuan</b>', '<b>W.P. Putrajaya</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=False)
fig.add_hline(y=df_joh['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=1, col=1)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=False)
#Graph 2
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=False)
fig.add_hline(y=df_ked['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=1, col=2)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=False)
#Graph 3
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=False)
fig.add_hline(y=df_kel['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=1, col=3)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=False)
#Graph 4
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=False)
fig.add_hline(y=df_mel['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=1, col=4)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=False)
#Graph 5
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=False)
fig.add_hline(y=df_neg['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=2, col=1)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=False)
#Graph 6
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=False)
fig.add_hline(y=df_pah['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=2, col=2)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=False)
#Graph 7
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=False)
fig.add_hline(y=df_prk['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=2, col=3)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=False)
#Graph 8
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=False)
fig.add_hline(y=df_per['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=2, col=4)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=False)
#Graph 9
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=3, col=1,secondary_y=False)
fig.add_hline(y=df_pen['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=3, col=1)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=3, col=1,secondary_y=False)
#Graph 10
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=3, col=2,secondary_y=False)
fig.add_hline(y=df_sab['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=3, col=2)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=3, col=2,secondary_y=False)
#Graph 11
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=3, col=3,secondary_y=False)
fig.add_hline(y=df_sar['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=3, col=3)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=3, col=3,secondary_y=False)
#Graph 12
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=3, col=4,secondary_y=False)
fig.add_hline(y=df_sel['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=3, col=4)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=3, col=4,secondary_y=False)
#Graph 13
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=4, col=1,secondary_y=False)
fig.add_hline(y=df_ter['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=4, col=1)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=4, col=1,secondary_y=False)
#Graph 14
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=4, col=2,secondary_y=False)
fig.add_hline(y=df_kul['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=4, col=2)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=4, col=2,secondary_y=False)
#Graph 15
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=4, col=3,secondary_y=False)
fig.add_hline(y=df_lab['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=4, col=3)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=4, col=3,secondary_y=False)
#Graph 16
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['deaths_new_dod'],name='Total Deaths',
                        line = dict(color='red', width=0.75)), row=4, col=4,secondary_y=False)
fig.add_hline(y=df_put['deaths_new_dod'].mean(), line_dash="dot",line_color="red", annotation_text="Average Deaths", 
              annotation_position="top left",row=4, col=4)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['deaths_vax_total'],name='Total Deaths Vax',
                        line = dict(color='blue', width=0.75)), row=4, col=4,secondary_y=False)
#Adding title and adjusting the layout
fig.update_layout(height=1000,showlegend=False,title_text="Covid19 Daily Deaths Total & Vaccinated At Each States",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()

#Extracting And Separating Data For Each States
#Johor
df = df_states_vaksin
df_joh = df[df.state == 'Johor']
df_joh['cum_partial'] = df_joh['daily_partial'].cumsum()
df_joh['cum_full'] = df_joh['daily_full'].cumsum()
df_joh['cum_booster'] = df_joh['daily_booster'].cumsum()
#Kedah
df_ked = df[df.state == 'Kedah']
df_ked['cum_partial'] = df_ked['daily_partial'].cumsum()
df_ked['cum_full'] = df_ked['daily_full'].cumsum()
df_ked['cum_booster'] = df_ked['daily_booster'].cumsum()
#Kelantan
df_kel = df[df.state == 'Kelantan']
df_kel['cum_partial'] = df_kel['daily_partial'].cumsum()
df_kel['cum_full'] = df_kel['daily_full'].cumsum()
df_kel['cum_booster'] = df_kel['daily_booster'].cumsum()
#Melaka
df_mel = df[df.state == 'Melaka']
df_mel['cum_partial'] = df_mel['daily_partial'].cumsum()
df_mel['cum_full'] = df_mel['daily_full'].cumsum()
df_mel['cum_booster'] = df_mel['daily_booster'].cumsum()
#Negeri Sembilan
df_neg = df[df.state == 'Negeri Sembilan']
df_neg['cum_partial'] = df_neg['daily_partial'].cumsum()
df_neg['cum_full'] = df_neg['daily_full'].cumsum()
df_neg['cum_booster'] = df_neg['daily_booster'].cumsum()
#Pahang
df_pah = df[df.state == 'Pahang']
df_pah['cum_partial'] = df_pah['daily_partial'].cumsum()
df_pah['cum_full'] = df_pah['daily_full'].cumsum()
df_pah['cum_booster'] = df_pah['daily_booster'].cumsum()
#Perak
df_prk = df[df.state == 'Perak']
df_prk['cum_partial'] = df_prk['daily_partial'].cumsum()
df_prk['cum_full'] = df_prk['daily_full'].cumsum()
df_prk['cum_booster'] = df_prk['daily_booster'].cumsum()
#Perlis
df_per = df[df.state == 'Perlis']
df_per['cum_partial'] = df_per['daily_partial'].cumsum()
df_per['cum_full'] = df_per['daily_full'].cumsum()
df_per['cum_booster'] = df_per['daily_booster'].cumsum()
#Pulau Pinang
df_pen = df[df.state == 'Pulau Pinang']
df_pen['cum_partial'] = df_pen['daily_partial'].cumsum()
df_pen['cum_full'] = df_pen['daily_full'].cumsum()
df_pen['cum_booster'] = df_pen['daily_booster'].cumsum()
#Sabah
df_sab = df[df.state == 'Sabah']
df_sab['cum_partial'] = df_sab['daily_partial'].cumsum()
df_sab['cum_full'] = df_sab['daily_full'].cumsum()
df_sab['cum_booster'] = df_sab['daily_booster'].cumsum()
#Sarawak
df_sar = df[df.state == 'Sarawak']
df_sar['cum_partial'] = df_sar['daily_partial'].cumsum()
df_sar['cum_full'] = df_sar['daily_full'].cumsum()
df_sar['cum_booster'] = df_sar['daily_booster'].cumsum()
#Selangor
df_sel = df[df.state == 'Selangor']
df_sel['cum_partial'] = df_sel['daily_partial'].cumsum()
df_sel['cum_full'] = df_sel['daily_full'].cumsum()
df_sel['cum_booster'] = df_sel['daily_booster'].cumsum()
#Terengganu
df_ter = df[df.state == 'Terengganu']
df_ter['cum_partial'] = df_ter['daily_partial'].cumsum()
df_ter['cum_full'] = df_ter['daily_full'].cumsum()
df_ter['cum_booster'] = df_ter['daily_booster'].cumsum()
#Kuala Lumpur
df_kul = df[df.state == 'W.P. Kuala Lumpur']
df_kul['cum_partial'] = df_kul['daily_partial'].cumsum()
df_kul['cum_full'] = df_kul['daily_full'].cumsum()
df_kul['cum_booster'] = df_kul['daily_booster'].cumsum()
#Labuan
df_lab = df[df.state == 'W.P. Labuan']
df_lab['cum_partial'] = df_lab['daily_partial'].cumsum()
df_lab['cum_full'] = df_lab['daily_full'].cumsum()
df_lab['cum_booster'] = df_lab['daily_booster'].cumsum()
#Putrajaya
df_put = df[df.state == 'W.P. Putrajaya']
df_put['cum_partial'] = df_put['daily_partial'].cumsum()
df_put['cum_full'] = df_put['daily_full'].cumsum()
df_put['cum_booster'] = df_put['daily_booster'].cumsum()

fig = make_subplots(rows=4, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}]],
                    
subplot_titles=('<b>Johor</b>', '<b>Kedah</b>', '<b>Kelantan</b>', '<b>Melaka</b>',
                '<b>Negeri Sembilan</b>', '<b>Pahang</b>', '<b>Perak</b>', '<b>Perlis</b>',
                '<b>Pulau Pinang</b>', '<b>Sabah</b>', '<b>Sarawak</b>', '<b>Selangor</b>',
                '<b>Terengganu</b>', '<b>W.P. Kuala Lumpur</b>', '<b>W.P. Labuan</b>', '<b>W.P. Putrajaya</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=1, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=1, col=1,secondary_y=False)
#Graph 2
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=1, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=1, col=2,secondary_y=False)
#Graph 3
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=1, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=1, col=3,secondary_y=False)
#Graph 4
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=1, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=1, col=4,secondary_y=False)
#Graph 5
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=2, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=2, col=1,secondary_y=False)
#Graph 6
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=2, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=2, col=2,secondary_y=False)
#Graph 7
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=2, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=2, col=3,secondary_y=False)
#Graph 8
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=2, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=2, col=4,secondary_y=False)
#Graph 9
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=3, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=3, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=3, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=3, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=3, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=3, col=1,secondary_y=False)
#Graph 10
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=3, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=3, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=3, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=3, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=3, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=3, col=2,secondary_y=False)
#Graph 11
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=3, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=3, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=3, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=3, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=3, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=3, col=3,secondary_y=False)
#Graph 12
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=3, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=3, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=3, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=3, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=3, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=3, col=4,secondary_y=False)
#Graph 13
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=4, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=4, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=4, col=1,secondary_y=True)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=4, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=4, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=4, col=1,secondary_y=False)
#Graph 14
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=4, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=4, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=4, col=2,secondary_y=True)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=4, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=4, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=4, col=2,secondary_y=False)
#Graph 15
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=4, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=4, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=4, col=3,secondary_y=True)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=4, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=4, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=4, col=3,secondary_y=False)
#Graph 16
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['daily_full'],name='Full',
                        line = dict(color='blue', width=0.75)), row=4, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['daily_partial'],name='Half',
                        line = dict(color='red', width=0.75)), row=4, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['daily_booster'],name='Booster',
                        line = dict(color='green', width=0.75)), row=4, col=4,secondary_y=True)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cum_full'],name='Full',
                        line = dict(color='blue', width=1)), row=4, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cum_partial'],name='Half',
                        line = dict(color='red', width=1)), row=4, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['cum_booster'],name='Booster',
                        line = dict(color='green', width=1)), row=4, col=4,secondary_y=False)
#Adding title and adjusting the layout
fig.update_layout(height=1000,showlegend=False,title_text="Covid19 Daily Vaccination (1D, 2D & 3D) At Each States",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()

dfpop = df_states_pop
#Creating new vax percentage columns at each states
#Johor
df_joh_pop = dfpop[dfpop.state == 'Johor']
df_joh_pop = df_joh_pop.at[df_joh_pop.index[0],'pop']
df_joh['2 Dos (%)'] = (df_joh['cum_full']/df_joh_pop)*100
#Kedah
df_ked_pop = dfpop[dfpop.state == 'Kedah']
df_ked_pop = df_ked_pop.at[df_ked_pop.index[0],'pop']
df_ked['2 Dos (%)'] = (df_ked['cum_full']/df_ked_pop)*100
#Kelantan
df_kel_pop = dfpop[dfpop.state == 'Kelantan']
df_kel_pop = df_kel_pop.at[df_kel_pop.index[0],'pop']
df_kel['2 Dos (%)'] = (df_kel['cum_full']/df_kel_pop)*100
#Melaka
df_mel_pop = dfpop[dfpop.state == 'Melaka']
df_mel_pop = df_mel_pop.at[df_mel_pop.index[0],'pop']
df_mel['2 Dos (%)'] = (df_mel['cum_full']/df_mel_pop)*100
#Negeri Sembilan
df_neg_pop = dfpop[dfpop.state == 'Negeri Sembilan']
df_neg_pop = df_neg_pop.at[df_neg_pop.index[0],'pop']
df_neg['2 Dos (%)'] = (df_neg['cum_full']/df_neg_pop)*100
#Pahang
df_pah_pop = dfpop[dfpop.state == 'Pahang']
df_pah_pop = df_pah_pop.at[df_pah_pop.index[0],'pop']
df_pah['2 Dos (%)'] = (df_pah['cum_full']/df_pah_pop)*100
#Perak
df_prk_pop = dfpop[dfpop.state == 'Perak']
df_prk_pop = df_prk_pop.at[df_prk_pop.index[0],'pop']
df_prk['2 Dos (%)'] = (df_prk['cum_full']/df_prk_pop)*100
#Perlis
df_per_pop = dfpop[dfpop.state == 'Perlis']
df_per_pop = df_per_pop.at[df_per_pop.index[0],'pop']
df_per['2 Dos (%)'] = (df_per['cum_full']/df_per_pop)*100
#Penang
df_pen_pop = dfpop[dfpop.state == 'Pulau Pinang']
df_pen_pop = df_pen_pop.at[df_pen_pop.index[0],'pop']
df_pen['2 Dos (%)'] = (df_pen['cum_full']/df_pen_pop)*100
#Sabah
df_sab_pop = dfpop[dfpop.state == 'Sabah']
df_sab_pop = df_sab_pop.at[df_sab_pop.index[0],'pop']
df_sab['2 Dos (%)'] = (df_sab['cum_full']/df_sab_pop)*100
#Sarawak
df_sar_pop = dfpop[dfpop.state == 'Sarawak']
df_sar_pop = df_sar_pop.at[df_sar_pop.index[0],'pop']
df_sar['2 Dos (%)'] = (df_sar['cum_full']/df_sar_pop)*100
#Selangor
df_sel_pop = dfpop[dfpop.state == 'Selangor']
df_sel_pop = df_sel_pop.at[df_sel_pop.index[0],'pop']
df_sel['2 Dos (%)'] = (df_sel['cum_full']/df_sel_pop)*100
#Terengganu
df_ter_pop = dfpop[dfpop.state == 'Terengganu']
df_ter_pop = df_ter_pop.at[df_ter_pop.index[0],'pop']
df_ter['2 Dos (%)'] = (df_ter['cum_full']/df_ter_pop)*100
#Kuala Lumpur
df_kul_pop = dfpop[dfpop.state == 'W.P. Kuala Lumpur']
df_kul_pop = df_kul_pop.at[df_kul_pop.index[0],'pop']
df_kul['2 Dos (%)'] = (df_kul['cum_full']/df_kul_pop)*100
#Labuan
df_lab_pop = dfpop[dfpop.state == 'W.P. Labuan']
df_lab_pop = df_lab_pop.at[df_lab_pop.index[0],'pop']
df_lab['2 Dos (%)'] = (df_lab['cum_full']/df_lab_pop)*100
#Putrajaya
df_put_pop = dfpop[dfpop.state == 'W.P. Putrajaya']
df_put_pop = df_put_pop.at[df_put_pop.index[0],'pop']
df_put['2 Dos (%)'] = (df_put['cum_full']/df_put_pop)*100
#Start creating the graph
fig = make_subplots(rows=4, cols=4, specs=[[{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}]],
                    
subplot_titles=('<b>Johor</b>', '<b>Kedah</b>', '<b>Kelantan</b>', '<b>Melaka</b>',
                '<b>Negeri Sembilan</b>', '<b>Pahang</b>', '<b>Perak</b>', '<b>Perlis</b>',
                '<b>Pulau Pinang</b>', '<b>Sabah</b>', '<b>Sarawak</b>', '<b>Selangor</b>',
                '<b>Terengganu</b>', '<b>W.P. Kuala Lumpur</b>', '<b>W.P. Labuan</b>', '<b>W.P. Putrajaya</b>'))
#Graph 1
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_joh['date'], y = df_joh['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=1, col=1)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=1, col=1)
#Graph 2
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ked['date'], y = df_ked['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=1, col=2)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=1, col=2)
#Graph 3
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kel['date'], y = df_kel['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=1, col=3)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=1, col=3)
#Graph 4
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mel['date'], y = df_mel['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=1, col=4)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=1, col=4)
#Graph 5
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_neg['date'], y = df_neg['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=2, col=1)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=2, col=1)
#Graph 6
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pah['date'], y = df_pah['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=2, col=2)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=2, col=2)
#Graph 7
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_prk['date'], y = df_prk['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=2, col=3)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=2, col=3)
#Graph 8
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_per['date'], y = df_per['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=2, col=4)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=2, col=4)
#Graph 9
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=3, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_pen['date'], y = df_pen['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=3, col=1,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=3, col=1)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=3, col=1)
#Graph 10
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=3, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sab['date'], y = df_sab['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=3, col=2,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=3, col=2)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=3, col=2)
#Graph 11
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=3, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sar['date'], y = df_sar['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=3, col=3,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=3, col=3)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=3, col=3)
#Graph 12
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=3, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sel['date'], y = df_sel['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=3, col=4,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=3, col=4)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=3, col=4)
#Graph 13
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=4, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ter['date'], y = df_ter['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=4, col=1,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=4, col=1)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=4, col=1)
#Graph 14
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=4, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_kul['date'], y = df_kul['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=4, col=2,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=4, col=2)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=4, col=2)
#Graph 15
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=4, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lab['date'], y = df_lab['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=4, col=3,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=4, col=3)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=4, col=3)
#Graph 16
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['2 Dos (%)'],name='2 Dos (%)',
                        line = dict(color='red', width=0.75)), row=4, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_put['date'], y = df_put['daily'],name='Total Daily Vax',
                        line = dict(color='blue', width=0.75)), row=4, col=4,secondary_y=True)
fig.add_shape(type="line", line_color="salmon", line_width=3, opacity=1, line_dash="dot",
              x0='2021-3-1', x1=df_date_end.at[df_date_end.index[0],'date'], xref="x", y0=90, y1=90, 
              yref="y", row=4, col=4)
fig.add_annotation(text='90% Vax. Pop.', x='2021-3-1', y=90, arrowhead=1, arrowsize=1, arrowwidth=1,
                   ax=50, ay=-10, showarrow=True, xanchor="left", yanchor="bottom", row=4, col=4)
#Adding title and adjusting the layout
fig.update_layout(height=1000,showlegend=False,
                  title_text="Covid19 Vaccination Against Vaccination Per Population At Each States",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()

#Retrieving dataset for Covid19 for all countries
dfworld = pd.read_csv('https://raw.githubusercontent.com/datasets/covid-19/main/data/time-series-19-covid-combined.csv')
dfworld['daily_cases'] = dfworld['Confirmed'].diff()
dfworld['daily_recover'] = dfworld['Recovered'].diff()
dfworld['daily_deaths'] = dfworld['Deaths'].diff()

dfworld2 = pd.read_csv('https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/latest/owid-covid-latest.csv')
dfworld2 = dfworld2[dfworld2['continent'].notna()] 
dfworld2 = dfworld2[['location','total_cases','total_deaths','people_vaccinated',
                     'population','population_density','gdp_per_capita']]
dfworld2['Cases (%)'] = (dfworld2['total_cases']/dfworld2['population'])*100
dfworld2['Deaths (%)'] = (dfworld2['total_deaths']/dfworld2['total_cases'])*100
dfworld2['Vaccination (%)'] = (dfworld2['people_vaccinated']/dfworld2['population'])*100

dfworld2.rename(columns={'location':'Country','total_cases':'Total Cases','total_deaths':'Total Deaths',
                         'people_vaccinated':'People Vaccinated','population':'Population',
                         'population_density':'Population Density','gdp_per_capita':'GDP Per Kapita'},inplace=True)

df_asean = dfworld2.loc[dfworld2['Country'].isin(['Malaysia','Singapore','Thailand','Indonesia',
                                              'Philippines','Cambodia','Laos','Vietnam',
                                              'Myanmar','Brunei'])]
df_asean = df_asean.sort_values('Total Cases',ascending=False)
def highlight_max(s):
    '''
    highlight the maximum in a Series Salmon.
    '''
    is_max = s == s.max()
    return ['background-color: salmon' if v else '' for v in is_max]

df_asean.style.set_caption("Overview Of Covid19 Cases In ASEAN Countries").set_table_styles([{
    'selector': 'caption',
    'props': [
        ('color', 'black'),
        ('font-size', '26px'),
        ("text-align", "center"),
        ('text-decoration', 'underline'),
        ('font-family','Arial'),
        ('text-shadow', '2px 2px 5px grey')
    ]},(dict
        (selector='th',props=[('text-align',
                               'left')]))]).format(
    {'Total Cases':'{:,.0f}','Total Deaths':'{:,.0f}','People Vaccinated':'{:,.0f}','Population':'{:,.0f}',
     'Population Density':'{:,.1f}','GDP Per Kapita':'{:,.1f}','Cases (%)':'{:,.1f}','Deaths (%)':'{:,.1f}',
     'Vaccination (%)':'{:,.1f}'}).set_properties(subset=['Country'],**{'text-align': 'left'}).hide_index().apply(
    highlight_max,subset=['Total Cases','Total Deaths','People Vaccinated','Population','Population Density',
                          'GDP Per Kapita','Cases (%)','Deaths (%)','Vaccination (%)'])

Overview Of Covid19 Cases In ASEAN Countries

Country	Total Cases	Total Deaths	People Vaccinated	Population	Population Density	GDP Per Kapita	Cases (%)	Deaths (%)	Vaccination (%)
Vietnam	7,791,841	41,817	79,686,878	98,168,829	308.1	6,171.9	7.9	0.5	81.2
Indonesia	5,956,561	153,599	194,437,519	276,361,788	145.7	11,188.7	2.2	2.6	70.4
Malaysia	3,974,019	34,329	27,397,802	32,776,195	96.3	26,808.2	12.1	0.9	83.6
Philippines	3,673,717	58,023	69,535,753	111,046,910	351.9	7,599.2	3.3	1.6	62.6
Thailand	3,353,969	24,246	54,791,560	69,950,844	135.1	16,277.7	4.8	0.7	78.3
Singapore	1,007,158	1,194	4,998,970	5,453,600	7,915.7	85,535.4	18.5	0.1	91.7
Myanmar	608,763	19,423	25,195,819	54,806,014	81.7	5,591.6	1.1	3.2	46.0
Laos	151,956	645	5,452,875	7,379,358	29.7	6,397.4	2.1	0.4	73.9
Cambodia	134,889	3,050	14,774,115	16,946,446	90.7	3,645.1	0.8	2.3	87.2
Brunei	123,422	180	407,876	441,532	81.3	71,809.3	28.0	0.1	92.4

#Selecting datasets for ASEAN Countries
#Malaysia
dfworld.rename(columns={'Country/Region':'Country'},inplace=True)
df_mas = dfworld[dfworld.Country == 'Malaysia']
df_mas = df_mas.reset_index(drop=True)
df_mas = df_mas.drop([0])
#Indonesia
df_ind = dfworld[dfworld.Country == 'Indonesia']
df_ind = df_ind.reset_index(drop=True)
df_ind = df_ind.drop([0])
#Philippines
df_phi = dfworld[dfworld.Country == 'Philippines']
df_phi = df_phi.reset_index(drop=True)
df_phi = df_phi.drop([0])
#Burma
df_bur = dfworld[dfworld.Country == 'Burma']
df_bur = df_bur.reset_index(drop=True)
df_bur = df_bur.drop([0])
#Singapore
df_sin = dfworld[dfworld.Country == 'Singapore']
df_sin = df_sin.reset_index(drop=True)
df_sin = df_sin.drop([0])
#Thailand
df_tha = dfworld[dfworld.Country == 'Thailand']
df_tha = df_tha.reset_index(drop=True)
df_tha = df_tha.drop([0])
#Vietnam
df_vie = dfworld[dfworld.Country == 'Vietnam']
df_vie = df_vie.reset_index(drop=True)
df_vie = df_vie.drop([0])
#Cambodia
df_cam = dfworld[dfworld.Country == 'Cambodia']
df_cam = df_cam.reset_index(drop=True)
df_cam = df_cam.drop([0])
#Brunei
df_bru = dfworld[dfworld.Country == 'Brunei']
df_bru = df_bru.reset_index(drop=True)
df_bru = df_bru.drop([0])
#Laos
df_lao = dfworld[dfworld.Country == 'Laos']
df_lao = df_lao.reset_index(drop=True)
df_lao = df_lao.drop([0])

#Creating the ASEAN Covid19 Cases Graphs
fig = make_subplots(rows=2, cols=5, specs=[[{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}]],
                    
subplot_titles=('<b>Malaysia</b>', '<b>Indonesia</b>', '<b>Philippines</b>', '<b>Burma</b>', '<b>Singapore</b>',
                '<b>Thailand</b>', '<b>Vietnam</b>', '<b>Cambodia</b>', '<b>Brunei</b>', '<b>Laos</b>'))
#Row 1
#Graph 1
fig.add_trace(go.Scatter(x = df_mas['Date'], y = df_mas['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas['Date'], y = df_mas['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=1, col=1,secondary_y=True)
#Graph 2
fig.add_trace(go.Scatter(x = df_ind['Date'], y = df_ind['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ind['Date'], y = df_ind['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=1, col=2,secondary_y=True)
#Graph 3
fig.add_trace(go.Scatter(x = df_phi['Date'], y = df_phi['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_phi['Date'], y = df_phi['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=1, col=3,secondary_y=True)
#Graph 4
fig.add_trace(go.Scatter(x = df_bur['Date'], y = df_bur['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_bur['Date'], y = df_bur['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=1, col=4,secondary_y=True)
#Graph 5
fig.add_trace(go.Scatter(x = df_sin['Date'], y = df_sin['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=1, col=5,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sin['Date'], y = df_sin['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=1, col=5,secondary_y=True)
#Row 2
#Graph 6
fig.add_trace(go.Scatter(x = df_tha['Date'], y = df_tha['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_tha['Date'], y = df_tha['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=2, col=1,secondary_y=True)
#Graph 7
fig.add_trace(go.Scatter(x = df_vie['Date'], y = df_vie['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_vie['Date'], y = df_vie['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=2, col=2,secondary_y=True)
#Graph 8
fig.add_trace(go.Scatter(x = df_cam['Date'], y = df_cam['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_cam['Date'], y = df_cam['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=2, col=3,secondary_y=True)
#Graph 9
fig.add_trace(go.Scatter(x = df_bru['Date'], y = df_bru['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_bru['Date'], y = df_bru['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=2, col=4,secondary_y=True)
#Graph 10
fig.add_trace(go.Scatter(x = df_lao['Date'], y = df_lao['Confirmed'],name='Total Cases',
                        line = dict(color='red', width=2)), row=2, col=5,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lao['Date'], y = df_lao['daily_cases'],name='Daily Cases',
                        line = dict(color='blue', width=0.75)), row=2, col=5,secondary_y=True)

#Adding title and adjusting the layout
fig.update_layout(height=500,showlegend=False,title_text="Covid19 Total And Daily Cases At ASEAN Countries",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()

#Creating the ASEAN Covid19 Cases Graphs
fig = make_subplots(rows=2, cols=5, specs=[[{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}],
                                           [{'secondary_y': True}, {'secondary_y': True},
                                            {'secondary_y': True}, {'secondary_y': True}, {'secondary_y': True}]],
                    
subplot_titles=('<b>Malaysia</b>', '<b>Indonesia</b>', '<b>Philippines</b>', '<b>Burma</b>', '<b>Singapore</b>',
                '<b>Thailand</b>', '<b>Vietnam</b>', '<b>Cambodia</b>', '<b>Brunei</b>', '<b>Laos</b>'))
#Row 1
#Graph 1
fig.add_trace(go.Scatter(x = df_mas['Date'], y = df_mas['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=1, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_mas['Date'], y = df_mas['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=1,secondary_y=True)
#Graph 2
fig.add_trace(go.Scatter(x = df_ind['Date'], y = df_ind['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=1, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_ind['Date'], y = df_ind['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=2,secondary_y=True)
#Graph 3
fig.add_trace(go.Scatter(x = df_phi['Date'], y = df_phi['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=1, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_phi['Date'], y = df_phi['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=3,secondary_y=True)
#Graph 4
fig.add_trace(go.Scatter(x = df_bur['Date'], y = df_bur['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=1, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_bur['Date'], y = df_bur['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=4,secondary_y=True)
#Graph 5
fig.add_trace(go.Scatter(x = df_sin['Date'], y = df_sin['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=1, col=5,secondary_y=False)
fig.add_trace(go.Scatter(x = df_sin['Date'], y = df_sin['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=1, col=5,secondary_y=True)
#Row 2
#Graph 6
fig.add_trace(go.Scatter(x = df_tha['Date'], y = df_tha['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=2, col=1,secondary_y=False)
fig.add_trace(go.Scatter(x = df_tha['Date'], y = df_tha['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=1,secondary_y=True)
#Graph 7
fig.add_trace(go.Scatter(x = df_vie['Date'], y = df_vie['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=2, col=2,secondary_y=False)
fig.add_trace(go.Scatter(x = df_vie['Date'], y = df_vie['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=2,secondary_y=True)
#Graph 8
fig.add_trace(go.Scatter(x = df_cam['Date'], y = df_cam['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=2, col=3,secondary_y=False)
fig.add_trace(go.Scatter(x = df_cam['Date'], y = df_cam['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=3,secondary_y=True)
#Graph 9
fig.add_trace(go.Scatter(x = df_bru['Date'], y = df_bru['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=2, col=4,secondary_y=False)
fig.add_trace(go.Scatter(x = df_bru['Date'], y = df_bru['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=4,secondary_y=True)
#Graph 10
fig.add_trace(go.Scatter(x = df_lao['Date'], y = df_lao['Deaths'],name='Total Deaths',
                        line = dict(color='blue', width=2)), row=2, col=5,secondary_y=False)
fig.add_trace(go.Scatter(x = df_lao['Date'], y = df_lao['daily_deaths'],name=' Daily Deaths',
                        line = dict(color='red', width=0.75)), row=2, col=5,secondary_y=True)

#Adding title and adjusting the layout
fig.update_layout(height=500,showlegend=False,title_text="Covid19 Total Deaths And Daily Cases At ASEAN Countries",title_x=0.5)
fig.update_annotations(font=dict(family="Helvetica", size=10))
fig.update_layout(font=dict(family="Helvetica", size=12))
fig.update_xaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showgrid=False, zeroline=False, showline=True, linewidth=2, linecolor='black')
#Plotting the graph
fig.show()

dfworld3 = dfworld2.sort_values('Total Cases',ascending=False).head(10)

def highlight_max(s):
    '''
    highlight the maximum in a Series Salmon.
    '''
    is_max = s == s.max()
    return ['background-color: salmon' if v else '' for v in is_max]

dfworld3.style.set_caption("Overview Of Top 10 Highest Covid19 Cases Worldwide").set_table_styles([{
    'selector': 'caption',
    'props': [
        ('color', 'black'),
        ('font-size', '26px'),
        ("text-align", "center"),
        ('text-decoration', 'underline'),
        ('font-family','Arial'),
        ('text-shadow', '2px 2px 5px grey')
    ]},(dict
        (selector='th',props=[('text-align',
                               'left')]))]).format(
    {'Total Cases':'{:,.0f}','Total Deaths':'{:,.0f}','People Vaccinated':'{:,.0f}','Population':'{:,.0f}',
     'Population Density':'{:,.1f}','GDP Per Kapita':'{:,.1f}','Cases (%)':'{:,.1f}','Deaths (%)':'{:,.1f}',
     'Vaccination (%)':'{:,.1f}'}).set_properties(subset=['Country'],**{'text-align': 'left'}).hide_index().apply(
    highlight_max,subset=['Total Cases','Total Deaths','People Vaccinated','Population','Population Density',
                          'GDP Per Kapita','Cases (%)','Deaths (%)','Vaccination (%)'])

Overview Of Top 10 Highest Covid19 Cases Worldwide

Country	Total Cases	Total Deaths	People Vaccinated	Population	Population Density	GDP Per Kapita	Cases (%)	Deaths (%)	Vaccination (%)
United States	79,728,165	971,087	254,870,032	332,915,074	35.6	54,225.4	23.9	1.2	76.6
India	43,007,841	516,479	970,523,455	1,393,409,033	450.4	6,426.7	3.1	1.2	69.7
Brazil	29,624,435	657,389	180,265,232	213,993,441	25.0	14,103.5	13.8	2.2	84.2
France	24,108,645	140,972	54,008,101	67,422,000	122.6	38,605.7	35.8	0.6	80.1
United Kingdom	20,154,286	163,658	52,725,660	68,207,114	272.9	39,753.2	29.5	0.8	77.3
Germany	18,717,682	126,920	63,609,768	83,900,471	237.0	45,229.2	22.3	0.7	75.8
Russia	17,297,181	356,812	78,981,249	145,912,022	8.8	24,766.0	11.9	2.1	54.1
Turkey	14,680,836	97,182	57,750,663	85,042,736	104.9	25,129.3	17.3	0.7	67.9
Italy	13,800,179	157,692	50,695,198	60,367,471	205.9	35,220.1	22.9	1.1	84.0
Spain	11,324,637	101,703	41,112,436	46,745,211	93.1	34,272.4	24.2	0.9	88.0

dfworld3 = dfworld2.sort_values('People Vaccinated',ascending=False).head(10)

def highlight_max(s):
    '''
    highlight the maximum in a Series Salmon.
    '''
    is_max = s == s.max()
    return ['background-color: salmon' if v else '' for v in is_max]

dfworld3.style.set_caption("Overview Of Top 10 Highest Covid19 People Vaccinated Worldwide").set_table_styles([{
    'selector': 'caption',
    'props': [
        ('color', 'black'),
        ('font-size', '26px'),
        ("text-align", "center"),
        ('text-decoration', 'underline'),
        ('font-family','Arial'),
        ('text-shadow', '2px 2px 5px grey')
    ]},(dict
        (selector='th',props=[('text-align',
                               'left')]))]).format(
    {'Total Cases':'{:,.0f}','Total Deaths':'{:,.0f}','People Vaccinated':'{:,.0f}','Population':'{:,.0f}',
     'Population Density':'{:,.1f}','GDP Per Kapita':'{:,.1f}','Cases (%)':'{:,.1f}','Deaths (%)':'{:,.1f}',
     'Vaccination (%)':'{:,.1f}'}).set_properties(subset=['Country'],**{'text-align': 'left'}).hide_index().apply(
    highlight_max,subset=['Total Cases','Total Deaths','People Vaccinated','Population','Population Density',
                          'GDP Per Kapita','Cases (%)','Deaths (%)','Vaccination (%)'])

Overview Of Top 10 Highest Covid19 People Vaccinated Worldwide

Country	Total Cases	Total Deaths	People Vaccinated	Population	Population Density	GDP Per Kapita	Cases (%)	Deaths (%)	Vaccination (%)
China	130,199	4,638	1,272,537,000	1,444,216,102	147.7	15,308.7	0.0	3.6	88.1
India	43,007,841	516,479	970,523,455	1,393,409,033	450.4	6,426.7	3.1	1.2	69.7
United States	79,728,165	971,087	254,870,032	332,915,074	35.6	54,225.4	23.9	1.2	76.6
Indonesia	5,956,561	153,599	194,437,519	276,361,788	145.7	11,188.7	2.2	2.6	70.4
Brazil	29,624,435	657,389	180,265,232	213,993,441	25.0	14,103.5	13.8	2.2	84.2
Pakistan	1,521,888	30,329	128,074,138	225,199,929	255.6	5,034.7	0.7	2.0	56.9
Bangladesh	1,950,527	29,114	126,544,686	166,303,494	1,265.0	3,524.0	1.2	1.5	76.1
Japan	6,060,771	27,056	102,058,113	126,050,796	347.8	39,002.2	4.8	0.4	81.0
Mexico	5,629,814	321,931	85,502,104	130,262,220	66.4	17,336.5	4.3	5.7	65.6
Vietnam	7,791,841	41,817	79,686,878	98,168,829	308.1	6,171.9	7.9	0.5	81.2

dfworld3 = dfworld2.sort_values('Population',ascending=False).head(10)

def highlight_max(s):
    '''
    highlight the maximum in a Series Salmon.
    '''
    is_max = s == s.max()
    return ['background-color: salmon' if v else '' for v in is_max]

dfworld3.style.set_caption("Overview Of Top 10 Highest Population Countries Worldwide").set_table_styles([{
    'selector': 'caption',
    'props': [
        ('color', 'black'),
        ('font-size', '26px'),
        ("text-align", "center"),
        ('text-decoration', 'underline'),
        ('font-family','Arial'),
        ('text-shadow', '2px 2px 5px grey')
    ]},(dict
        (selector='th',props=[('text-align',
                               'left')]))]).format(
    {'Total Cases':'{:,.0f}','Total Deaths':'{:,.0f}','People Vaccinated':'{:,.0f}','Population':'{:,.0f}',
     'Population Density':'{:,.1f}','GDP Per Kapita':'{:,.1f}','Cases (%)':'{:,.1f}','Deaths (%)':'{:,.1f}',
     'Vaccination (%)':'{:,.1f}'}).set_properties(subset=['Country'],**{'text-align': 'left'}).hide_index().apply(
    highlight_max,subset=['Total Cases','Total Deaths','People Vaccinated','Population','Population Density',
                          'GDP Per Kapita','Cases (%)','Deaths (%)','Vaccination (%)'])

Overview Of Top 10 Highest Population Countries Worldwide

Country	Total Cases	Total Deaths	People Vaccinated	Population	Population Density	GDP Per Kapita	Cases (%)	Deaths (%)	Vaccination (%)
China	130,199	4,638	1,272,537,000	1,444,216,102	147.7	15,308.7	0.0	3.6	88.1
India	43,007,841	516,479	970,523,455	1,393,409,033	450.4	6,426.7	3.1	1.2	69.7
United States	79,728,165	971,087	254,870,032	332,915,074	35.6	54,225.4	23.9	1.2	76.6
Indonesia	5,956,561	153,599	194,437,519	276,361,788	145.7	11,188.7	2.2	2.6	70.4
Pakistan	1,521,888	30,329	128,074,138	225,199,929	255.6	5,034.7	0.7	2.0	56.9
Brazil	29,624,435	657,389	180,265,232	213,993,441	25.0	14,103.5	13.8	2.2	84.2
Nigeria	255,103	3,142	19,884,781	211,400,704	209.6	5,338.5	0.1	1.2	9.4
Bangladesh	1,950,527	29,114	126,544,686	166,303,494	1,265.0	3,524.0	1.2	1.5	76.1
Russia	17,297,181	356,812	78,981,249	145,912,022	8.8	24,766.0	11.9	2.1	54.1
Mexico	5,629,814	321,931	85,502,104	130,262,220	66.4	17,336.5	4.3	5.7	65.6

Notes:

All datasets in this reports were retrieved from:-

1. Malaysia MOH Github Account

2. CITF Github Account

3. COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University Github Account

End Of Report